Evaluation of Tidal Fresh Forest Distributions and Tropical Storm Impacts Using Sentinel-2 MSI Imagery

Situated in the transitional zone between non-tidal forests upstream and tidal fresh marshes downstream, tidal fresh forests occupy a unique and increasingly precarious habitat. The threat of intensifying anthropogenic climate change, compounded by the effects of historical logging and drainage alterations, could reduce the extent of this valuable ecosystem. The overall goals of this project were to identify forest communities present in the Altamaha tidal fresh forest; develop satellite imagery-based classifications of tidal fresh forest and tidal marsh vegetation along the Altamaha River, Georgia; and to quantify changes in vegetation distribution in the aftermath of hurricanes Matthew and Irma. Based on vegetation data gathered during our field survey, we identified at least eight distinct forest communities with hierarchical clustering methods. Using Sentinel-2 Multispectral Imager (MSI) satellite imagery and a balanced random forest classifier, we mapped land cover for six anniversary images from 2016 to 2021 to examine changes in vegetation distributions. Overall classification accuracies ranged from 80 to 86%, and we were able to accurately discriminate between several classes at the species level. Over our six year study period we did not observe any substantial changes in land cover, including the forest-marsh transition, suggesting resilience to tropical weather impacts. We postulate that this stasis may be due to the large volume of freshwater delivered by the Altamaha River and the extensive tidal marshes of the Altamaha estuary, which protect freshwater wetlands from the short-term effects of saltwater intrusion by reducing salinity and buffering them from acute pulse events such as hurricane storm surges

The impact of land use and land cover changes on wetland productivity and hydrological systems in the Limpopo transboundary river basin, South Africa

Philosophiae Doctor - PhDWetlands are highly productive systems that act as habitats for a variety of flora and fauna. Despite their ecohydrological significance, wetland ecosystems are under severe threat as a result of environmental changes (e.g. the changing temperature and rainfall), as well as pressure from anthropogenic land use activities (e.g. agriculture, rural-urban development and dam construction). Such changes result in severe disturbances in the hydrology, plant species composition, spatial distribution, productivity and diversity of wetlands, as well as their ability to offer critical ecosystem goods and services. However, wetland degradation varies considerably from place to place, with severe degradation occurring particularly in developing regions, such as sub-Saharan Africa, where Land Use and Land Cover changes impact on wetland ecosystems by affecting the diversity of plant species, productivity, as well as the wetland hydrology

The global tree carrying capacity (keynote)

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Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks

The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets

Research and Technology Objectives and Plans Summary (RTOPS)

The NASA research and technology program for FY 1990 is presented. The summary portions is compiled of each of the RTOPs (Research and Technology Objectives and Plans) used for management review and control of research currently in progress throughout NASA. The RTOP summary is designed to facilitate communication and coordination among concerned technical personnel in government, industry, and universities. The first section containing citations and abstracts of the RTOPs is followed by four indices: Subject; Technical Monitor; Responsible NASA Organization; and RTOP number